ImageStacking 📚

This notebook aims to explore the purpose of Image Stacking and observe how capturing multiple short exposures is advantegeous than one single exposure for Astrophotography.

Author: Timothy Do

References

[1] PhotographingSpace.com. (2023, December 22). Homework: Download and Stack my Data! [Online]. Available: https://www.photographingspace.com/homework-download-stack-data/
[2] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. ORB: an efficient alternative to SIFT or SURF. In 2011 IEEE International Conference on Computer Vision (ICCV), 1 pages 2564–2571. IEEE, 2011.
[3] AstroBackyard.com. (2024). Deep Sky Stacker Tutorial Practice Files. [Online]. Available: https://astrobackyard.com/dss-practice-files/

Dependencies

IN_COLAB = True
try: 
    import google.colab
except: 
    IN_COLAB = False
print(f'In CoLab: {IN_COLAB}')

In CoLab: False

if(IN_COLAB):
    !curl https://raw.githubusercontent.com/dotimothy/astronomy/main/requirements.txt -o ./requirements.txt
    !pip install -r requirements.txt
else:
    !pip install -r ../requirements.txt

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[notice] A new release of pip is available: 24.2 -> 24.3.1
[notice] To update, run: python.exe -m pip install --upgrade pip

# Dependencies
import os
import gc
import gdown
import numpy as np
import rawpy
import cv2 as cv
import matplotlib.pyplot as plt 
from tqdm import tqdm

# Global Helper Functions
# Crudely Normalize an Image (float) from 0-1
def normalize(img):
    return (img - img.min())/(img.max()-img.min())

# Contrast Stretches an Image (float)
def contrast_stretch(img,min=0,max=1):
    return np.clip(normalize(img) * (max-min) + min,a_min=0,a_max=1)

# Convert a uint8/uint16 image to a float image
def int_to_float(img):
    return normalize(img.astype(np.float))

# Convert a float image to a uint8 image
def float_to_int(img):
    return ((2**8-1)*normalize(img)).astype(np.uint8)

# Convert a float image to a uint16 image
def float_to_int16(img):
    return ((2**16-1)*normalize(img)).astype(np.uint16)

# Converts CR2 to Numpy Array
def cr2_to_numpy(cr2Path,uint=False,gamma=(1,1),half_size=True,use_camera_wb=False,no_auto_bright=False):
    with rawpy.imread(cr2Path) as raw:
        if(uint):
            out = raw.postprocess() # uint8
        else:
            out = raw.postprocess(gamma=gamma,use_camera_wb=use_camera_wb,use_auto_wb=True,no_auto_bright=no_auto_bright,half_size=half_size,output_bps=16,demosaic_algorithm=rawpy.DemosaicAlgorithm.VNG,output_color=rawpy.ColorSpace.sRGB)
            out = out.astype(np.float32)/ 2**16
        if(out.shape[2] > out.shape[1]): # Vertical, must rotate
            out = np.rot90(out,k=3)
    return out

def cr2Dir_to_tensor(stackPath,gamma=(1,1),use_camera_wb=False,half_size=True,no_auto_bright=False,exclude=[]):
    stackFiles = sorted([file for file in os.listdir(stackPath) if (file not in exclude) and (file.endswith('.CR2') or file.endswith('.CR3'))])
    print(f'Stack Files: {stackFiles}')
    print('Stacking Raw Images into a Tensor')
    stackedNumpy = np.stack([cr2_to_numpy(f'{stackPath}/{stackFile}',gamma=gamma,use_camera_wb=use_camera_wb,half_size=half_size,no_auto_bright=no_auto_bright) for stackFile in tqdm(stackFiles)])
    print(f'Stacked Numpy Shape: {stackedNumpy.shape}')
    print(f'Stacked Numpy Data Type: {stackedNumpy.dtype}')
    gc.collect()
    return stackedNumpy 

Downloading the Data

For exploring the process of Image Stacking, we will be downloading a "Homework" example from Cory Schmitz's blog in the PhotographSpace site! He takes a really nice shot of the milky way that we can work with. [1]

Here are some logisitcs of the stack, taken on a static tripod:

• Camera: Canon 5D Mark III
• ISO: 6400
• Exposure Length: 15 seconds
• White Balance: 4250K

# Creating Directory to Store the Data
dataDir1 = '../data/imagestacking/photographspace'
os.makedirs(dataDir1,exist_ok=True)

# Downloading the Single Raw Image & Full Stack
site = "https://www.photographingspace.com/downloads/"
singleRawFile = "CSM30803.CR2"
stackFile = "11x_ISO6400_f2.8_15s_5DMkIII_raw.zip"
for file in [singleRawFile,stackFile]: 
    filePath = f'{dataDir1}/{file}'
    if(not(os.path.exists(filePath))):
        fileLink = f'{site}/{file}'
        os.system(f'curl {fileLink} -o {filePath}')
    if(file.endswith('.zip')):
        zipDir = filePath.split('.zip')[0]
        if(not(os.path.exists(zipDir))):
            os.system(f'unzip {filePath} -d {zipDir}')

Visualizing the Data

# Extracting Single Raw Image & Viewing
singleRawPath = f'{dataDir1}/{singleRawFile}'
singleRaw = cr2_to_numpy(singleRawPath,use_camera_wb=True)
print(f'Single Raw Numpy Shape: {singleRaw.shape}')
print(f'Single Raw Numpy Data Type: {singleRaw.dtype}')
cv.imwrite(f"{dataDir1}/{singleRawFile.split('.CR2')[0]}.tiff",float_to_int16(singleRaw)[...,::-1])

# Plotting
plt.figure(figsize=(15,10))
plt.title(f'Single Raw: {singleRawFile}')
plt.imshow(contrast_stretch(singleRaw))
plt.axis('off')
plt.show()

Single Raw Numpy Shape: (1935, 2898, 3)
Single Raw Numpy Data Type: float64

# Read the Stacks into Memory
stackPath = f'{dataDir1}/11x_ISO6400_f2.8_15s_5DMkIII_raw/raw'
stackFiles = sorted([file for file in os.listdir(stackPath) if file.endswith('.CR2')])
stackedNumpy = cr2Dir_to_tensor(stackPath,use_camera_wb=True)

Stack Files: ['CSM30799.CR2', 'CSM30800.CR2', 'CSM30801.CR2', 'CSM30802.CR2', 'CSM30803.CR2', 'CSM30804.CR2', 'CSM30805.CR2', 'CSM30806.CR2', 'CSM30807.CR2', 'CSM30808.CR2', 'CSM30809.CR2']
Stacking Raw Images into a Tensor

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Stacked Numpy Shape: (11, 1935, 2898, 3)
Stacked Numpy Data Type: float64

# Viewing the Stack
rows = 5
cols = 2
fig, ax = plt.subplots(nrows=rows,ncols=cols,figsize=(15,30))
plt.suptitle('Raw Image Stack')
for i in range(rows):
    for j in range(cols):
        ax[i][j].axis('off')
        ax[i][j].set_title(stackFiles[(i*cols)+j])
        ax[i][j].imshow(contrast_stretch(stackedNumpy[(i*cols)+j]))
plt.tight_layout()
plt.show()

Stacking Methods

We will explore stacking methods in order to reduce noise and have the deep sky objects really pop out! We first start off with Naive methods, which work by avergaing the images to filter noise. The noise is coming from the shot, is due to photons, sensor readout, thermal, etc. The noise can modeled as additive as shown below: $$I(x,y) = f(x,y) + n(x,y)$$ $$n(x,y) \sim \mathcal{N}(\mu,\sigma^2)$$, where it is assumed zero-mean and unit variance (i.e., $\mu = 0, \sigma = 1$).

Sampling a large number of images estimates the distribution of the noise to filter it out. Averaging works in the ideal setting (i.e., when there is no parallax between scenes), but not in the real world because of star trails. Star trails are caused when the stars rotate with the Earth's rotation in perspective of the shot, so we need to perform image registartion before stacking the images!

Simulation

We first provide an ideal scene, where a test scene is generated with a simulated deep sky object.

# Helper Functions to Simulate Noise
# Creating Test Scene 
def createTestScene(width=32,height=32,radius=2):
    # Image dimensions
    # Create an empty image (black background)
    image = np.zeros((height, width,3))

    # Define circle parameters
    center_x = width // 2
    center_y = height // 2

    # Create a meshgrid
    y, x = np.ogrid[-center_y:height-center_y, -center_x:width-center_x]

    # Calculate distance from center
    dist_from_center = np.sqrt(x**2 + y**2)

    # Set pixels within the circle to white
    image[dist_from_center <= radius] = 1
    return image

# Genereates Noise with Zero Mean and Unit Variance 
def createNoise(width=32,height=32,dist='gauss'):
    if(dist == 'uniform'):
        noise = np.random.rand(height,width,3)
    elif(dist == 'gauss'):
        noise = np.random.randn(height,width,3)
    return normalize(noise)

testScene = createTestScene()
print(f'Test Scene Shape: {testScene.shape}')
print(f'Test Scene Data Type: {testScene.dtype}')
plt.figure(figsize=(15,10))
plt.imshow(testScene)
plt.title('Test Scene')
plt.axis('off')
plt.show()

Test Scene Shape: (32, 32, 3)
Test Scene Data Type: float64

testNoise = createNoise(dist='gauss')
print(f'Test Noise Shape: {testNoise.shape}')
print(f'Test Noise Data Type: {testNoise.dtype}')
print(f'Test Noise Min: {testNoise.min()}')
print(f'Test Noise Max: {testNoise.max()}')
plt.figure(figsize=(15,10))
plt.imshow(testNoise)
plt.title('Test Noise')
plt.axis('off')
plt.show()

Test Noise Shape: (32, 32, 3)
Test Noise Data Type: float64
Test Noise Min: 0.0
Test Noise Max: 1.0

# Generate the Noisy Stack 
scale = 1
numStack = 10000
print('Creating Test Stack...')
testStack = np.stack([normalize(testScene + scale*createNoise(dist='uniform')) for _ in tqdm(range(numStack))])
print(f'Test Stack Shape: {testStack.shape}')

Creating Test Stack...

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Test Stack Shape: (10000, 32, 32, 3)

plt.figure(figsize=(15,10))
plt.title('Test Stack with Added Noise')
plt.axis('off')
plt.imshow(contrast_stretch(testStack[0]))
plt.show()

# Viewing the Average Raw Image (Naive Stacking)
testMean = np.mean(testStack,axis=0)
# Plotting the Average
plt.figure(figsize=(15,10))
plt.title(f'Averaged Post-Processed Test Stack')
plt.imshow(contrast_stretch(testMean))
plt.axis('off')
plt.show()

# Viewing the Median Raw Image (Naive Stacking)
testMedian = np.median(testStack,axis=0)
# Plotting the Average
plt.figure(figsize=(15,10))
plt.title(f'Median Post-Processed Test Stack')
plt.imshow(contrast_stretch(testMedian))
plt.axis('off')
plt.show()

Real Images

As seen above, averaging the noisy image stack elimiantes the zero mean noise, that's because as many samples, it models the distribution of the noise, which is $\sigma(0,1)$. However, the assumption is the scene is static, which is not the case for the Earth's constant rotation.

Every day, the Earth approximately rotates by 360 degrees, translating to 0.25 degrees a minute. Naively stacking all the images leads to translational error, visually seen in the form of star trails as shown below.

# Viewing the Average Raw Image (Naive Stacking)
stackedMean = np.mean(stackedNumpy,axis=0)
# Plotting the Average
plt.figure(figsize=(15,10))
plt.title(f'Naive Stacking by Mean')
plt.imshow(contrast_stretch(stackedMean))
plt.axis('off')
plt.show()

# Viewing the Median Raw Image (Naive Stacking)
stackedMedian = np.median(stackedNumpy,axis=0)
# Plotting the Median
plt.figure(figsize=(15,10))
plt.title(f'Naive Stacking by Median')
plt.imshow(contrast_stretch(stackedMedian))
plt.axis('off')
plt.show()

Image Registration

In order to correctly stack many astrophotography long exposures, a preprocessing operation of image registration needs to occur to align the images. ORB Image registration finds common keypoints between an image and a reference, estimates the transformation matrix, and maps the new pixels with respect to the reference to correct rotation [2]. This will mitigate the prescence of star trails and allow us to accurately stack our images for a nice celestial object!

# Registers a Float Img (img2) with Respect to a Reference (img1)
def register_image(img1,img2,numFeatures=5000,match=0.9,refill=True):
    # Convert to uint8 grayscale.
    gray1 = cv.cvtColor(float_to_int(img1), cv.COLOR_BGR2GRAY)
    gray2 = cv.cvtColor(float_to_int(img2), cv.COLOR_BGR2GRAY)
    height, width = gray1.shape
    
    # Create ORB detector
    orb_detector = cv.ORB_create(numFeatures)
    
    # Find keypoints and descriptors.
    # The first arg is the image, second arg is the mask
    #  (which is not required in this case).
    kp1, d1 = orb_detector.detectAndCompute(gray1, None)
    kp2, d2 = orb_detector.detectAndCompute(gray2, None)
    
    # Match features between the two images.
    # We create a Brute Force matcher with 
    # Hamming distance as measurement mode.
    matcher = cv.BFMatcher(cv.NORM_HAMMING, crossCheck = True)
    
    # Match the two sets of descriptors.
    matches = list(matcher.match(d1, d2))

    
    # Sort matches on the basis of their Hamming distance.
    matches.sort(key = lambda x: x.distance)
    
    # Take the top matches forward.
    matches = matches[:int(len(matches)*match)]
    no_of_matches = len(matches)
    
    # Define empty matrices of shape no_of_matches * 2.
    p1 = np.zeros((no_of_matches, 2))
    p2 = np.zeros((no_of_matches, 2))
    
    for i in range(len(matches)):
      p1[i, :] = kp1[matches[i].queryIdx].pt
      p2[i, :] = kp2[matches[i].trainIdx].pt
    
    # Find the homography matrix.
    homography, mask = cv.findHomography(p1, p2, cv.RANSAC)
    
    # Use this matrix to transform the
    # colored image wrt the reference image.
    transformed_img = cv.warpPerspective(img1,
                        homography, (width, height))

    if(refill): 
        transformed_img = np.where(transformed_img != 0, transformed_img, img2) 
    
    return transformed_img

# Stacks Images with Automatic Alignment
def stack_images(imgStack,method='mean',align=True,refIndex=0.5,refill=True):
    if align:
        refIndex = len(imgStack) // 2 if refIndex == 0.5 else refIndex
        newImgs = []
        for i in tqdm(range(imgStack.shape[0])): 
            newImgs.append(normalize(imgStack[i]) if i == refIndex else register_image(imgStack[i],imgStack[refIndex],refill=refill))
        imgStack = np.stack(newImgs)
    stackedImg = np.median(imgStack,axis=0) if method == 'med' else np.mean(imgStack,axis=0)
    return stackedImg

# Stack Images with Automatic Alignment (Memory Saver with Mean Method)
def stack_images_opt(stackFiles,gamma=(1,1),use_camera_wb=False,half_size=True,no_auto_bright=False,align=True,refIndex=0.5,refill=True,stackedDark=None):
    stackedImg = 0
    rawSettings = (False,gamma,half_size,use_camera_wb,no_auto_bright)
    if align:
        refIndex = len(stackFiles) // 2 if refIndex == 0.5 else refIndex
        refImg = np.clip(cr2_to_numpy(stackFiles[refIndex],*rawSettings) - (stackedDark if stackedDark is not None else 0),a_min=0,a_max=1)
        for i in tqdm(range(len(stackFiles))):
            if i == refIndex:
                stackedImg += refImg/len(stackFiles) 
            else: 
                capImg = np.clip(cr2_to_numpy(stackFiles[i],*rawSettings) - (stackedDark if stackedDark is not None else 0),a_min=0,a_max=1)
                stackedImg += (register_image(capImg,refImg,refill=refill) if align else capImg)/len(stackFiles)
        gc.collect()
    return stackedImg

# Stacking the Astro Image
stackedFiles = sorted([f'{stackPath}/{file}' for file in os.listdir(stackPath) if file.endswith('.CR2')])
stackedImg = stack_images_opt(stackedFiles,use_camera_wb=True)

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11/11 [00:12<00:00,  1.14s/it]

# Plotting the Stacked Image
plt.figure(figsize=(15,10))
plt.title(f'Mean Stacking with ORB Alignment')
plt.imshow(contrast_stretch(stackedImg))
plt.axis('off')
plt.show()

# Writing the Final Result
cv.imwrite(f'{dataDir1}/stackedImg.tiff',float_to_int16(stackedImg)[...,::-1])
print('Saving the Final Result')

Saving the Final Result

Now with Image Registration, the Astrophoto stack was succesfully merged using Python! Feel free to apply any postprocessing tactics to make the images look nice!

# Freeing Up Old Tensors for the Next Experiment
del singleRaw,testScene,testNoise,testStack,testMean,testMedian,stackedNumpy,stackedImg
gc.collect()
print('Freeing up Memory for the next Experiment')

Freeing up Memory for the next Experiment

Another Example: Andromeda Galaxy (M31)

We will implement the full image stacking pipeline on Andromeda Galaxy (M31) data! This data has been provided by Jerry Lodriguss from AstroPix.

# Creating Directory to Store the Data
dataDir2 = f'../data/imagestacking/astropix'
os.makedirs(dataDir2,exist_ok=True)

# Downloading M31 Zip 
m31Link = 'https://drive.google.com/uc?id=19MZpu3mdGXuPvOXg_SIX-Ff57My3Wx8n'
m31Zip = f'{dataDir2}/M31.zip'
m31Dir = f'{dataDir2}/M31'
if(not(os.path.exists(m31Zip))):
    gdown.download(m31Link,output=m31Zip)
if(not(os.path.exists(m31Dir))):
    os.system(f'unzip {m31Zip} -d {m31Dir}')

# Extracting Single Raw Image & Viewing
singleRawFile = 'IMG_0702.CR2'
m31StackDir = f'{m31Dir}/M31 RAW 48F ISO800 8min'
singleRawPath = f'{m31StackDir}/{singleRawFile}'
singleRaw = cr2_to_numpy(singleRawPath,use_camera_wb=False,gamma=(3,3),no_auto_bright=True)
print(f'Single Raw Numpy Shape: {singleRaw.shape}')
print(f'Single Raw Numpy Data Type: {singleRaw.dtype}')
cv.imwrite(f"{dataDir2}/{singleRawFile.split('.CR2')[0]}.tiff",float_to_int16(singleRaw)[...,::-1])

# Plotting
plt.figure(figsize=(15,10))
plt.title(f'M31 Single Raw: {singleRawFile}')
plt.imshow(contrast_stretch(singleRaw))
plt.axis('off')
plt.show()

Single Raw Numpy Shape: (1738, 2604, 3)
Single Raw Numpy Data Type: float64

# Getting the Stacked Image
m31StackFiles = sorted([f'{m31StackDir}/{file}' for file in os.listdir(m31StackDir) if file.endswith('.CR2')])
m31StackedImg = stack_images_opt(m31StackFiles,gamma=(3,3),use_camera_wb=False,no_auto_bright=True)

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:06<00:00,  1.21it/s]

# Plotting the Stacked Image
plt.figure(figsize=(15,10))
plt.title(f'M31 Stacked Image')
plt.imshow(contrast_stretch(m31StackedImg))
plt.axis('off')
plt.show()

# Writing the Final Result
cv.imwrite(f'{dataDir2}/m31StackedImg.tiff',float_to_int16(m31StackedImg)[...,::-1])
print('Saving the Final Result')

Saving the Final Result

Incorporating Dark Calibration

In advanced astrophotography, an astronmer would usually take a set of calibration "dark" images. Dark images are defined as frames that are captured with the lens cap on to only capture the noise with the same camera settings (i.e., same ISO, apeture, shutter speed, etc.). Since the scene is assumed to be zero, the dark images is modeled as: $$I_{dark}(x,y) = n(x,y)$$

For this next section, we will use another dataset provided by Trevor Jones from AstroBackyard. He captures a really nice shot of the North American Nebula with Class 4 Bortle Skies with a Canon EOS Ra Sky-Walker Star Adventurer Star Tracker tracking the movement of the stars (mitigating image registration).

For ease of download, I provided the link for each raw image on Google Drive in a text file.

# Creating Directory to Store the Data
dataDir3 = '../data/imagestacking/astrobackyard'
os.makedirs(dataDir3,exist_ok=True)

# Downloading the AstroBackyard Dataset
astroBackyardDir = f'{dataDir3}/AstroBackyardNorthAmericanNebula'
lightDir = f'{astroBackyardDir}/LIGHTS'
darkDir = f'{astroBackyardDir}/DARKS'
if(not(os.path.exists(astroBackyardDir))):
    if(IN_COLAB): # Downloading GDrive Links for Each Photo
        !curl https://raw.githubusercontent.com/dotimothy/astronomy/main/analysis/AstroBackyardNorthAmericanNebulaLightLinks.txt -o ./AstroBackyardNorthAmericanNebulaLightLinks.txt
        !curl https://raw.githubusercontent.com/dotimothy/astronomy/main/analysis/AstroBackyardNorthAmericanNebulaDarkLinks.txt -o ./AstroBackyardNorthAmericanNebulaDarkLinks.txt
    with open('./AstroBackyardNorthAmericanNebulaLightLinks.txt','r') as lightLinks: # Downloading Light Images
        os.makedirs(lightDir,exist_ok=True)
        print(f'Downloading AstroBackyard Light Images')
        for line in tqdm(lightLinks.readlines()):
            lightLink = line.strip('\n')
            gdown.download(lightLink,output=lightDir,quiet=True)
    with open('./AstroBackyardNorthAmericanNebulaDarkLinks.txt','r') as darkLinks: # Downloading Dark Images
        os.makedirs(darkDir,exist_ok=True)
        print(f'Downloading Astrobackyard Dark Images')
        for line in tqdm(darkLinks.readlines()):
            darkLink = line.strip('\n')
            gdown.download(darkLink,output=darkDir,quiet=True)

testLight = cr2_to_numpy(f'{lightDir}/1R9A3692.CR3',use_camera_wb=False,no_auto_bright=True)
print(f'Test AstroBackyard Light Shape: {testLight.shape}')
print(f'Test AstroBackyard Data Type: {testLight.dtype}')
plt.figure(figsize=(15,10))
plt.imshow(testLight)
plt.axis('off')
plt.title('AstroBackyard North American Nebula Light')
plt.show()

Test AstroBackyard Light Shape: (2249, 3371, 3)
Test AstroBackyard Data Type: float64

# Test Dark
testDark = cr2_to_numpy(f'{darkDir}/1R9A3804.CR3',use_camera_wb=False,no_auto_bright=True)
print(f'Test AstroBackyard Dark Shape: {testDark.shape}')
print(f'Test AstroBackyard Data Type: {testDark.dtype}')
plt.figure(figsize=(15,10))
plt.imshow(testDark)
plt.axis('off')
plt.title('AstroBackyard North American Nebula Dark')
plt.show()

Test AstroBackyard Dark Shape: (2249, 3371, 3)
Test AstroBackyard Data Type: float64

# Getting the Tensor to Only Stack the Lights
toExclude = [f'1R9A{3740+i}.CR3' for i in range(3768-3740+1)] # Excluded Misoriented Lights
lightStackFiles = sorted([f'{lightDir}/{file}' for file in os.listdir(lightDir) if (file not in toExclude and (file.endswith('.CR2') or file.endswith('.CR3')))])
darkStackFiles = sorted([f'{darkDir}/{file}' for file in os.listdir(darkDir) if (file.endswith('.CR2') or file.endswith('.CR3'))])            

# Getting the Stacked Light (without Dark Calibration) 
stackedLight = stack_images_opt(lightStackFiles,use_camera_wb=False,no_auto_bright=True)

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [01:29<00:00,  1.12s/it]

# Plotting the Stacked Image without Dark Calibration
plt.figure(figsize=(15,10))
plt.title(f'AstroBackground Mean Stacking without Dark Calibration')
plt.imshow(contrast_stretch(stackedLight))
plt.axis('off')
plt.show()

# Stacking the Darks
stackedDark = stack_images_opt(darkStackFiles,use_camera_wb=False,no_auto_bright=True)

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [00:13<00:00,  1.04it/s]

# Stacking Image with Dark Calibration
stackedImg = stack_images_opt(lightStackFiles,use_camera_wb=False,no_auto_bright=True,stackedDark=stackedDark)

100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [01:28<00:00,  1.10s/it]

# Plotting the Stacked Image with Dark Calibration
plt.figure(figsize=(15,10))
plt.title(f'AstroBackground Mean Stacking with Dark Calibration')
plt.imshow(contrast_stretch(stackedImg))
plt.axis('off')
plt.show()

# Writing the Final Result
cv.imwrite(f'{dataDir3}/stackedImg.tiff',float_to_int16(stackedImg)[...,::-1])
print('Saving the Final Result')

Saving the Final Result

Custom Shots

Here are some results processed on some custom images. One scene in particular is taken during the great comet C/2023 A3 peak in Mt. Pinos by my buddy Sam Wagner from UCLA.

# Custom Data Dir
customDataDir = '../data/custom'
os.makedirs(customDataDir,exist_ok=True)

# Downloading Sam's Images
mtPinosDir = f'{customDataDir}/mtPinos'
mtPinosLink = f'https://drive.google.com/drive/folders/17u_iLlmqdeTJljULIXKjFjtoGfOrzZCO'
if(not(os.path.exists(mtPinosDir))):
    gdown.download_folder(mtPinosLink,output=mtPinosDir)

Retrieving folder contents

Processing file 1rVbyYAovN0W8dsfIsV-6OUBisj4mbApm DSC08770.ARW
Processing file 1eL1pyIDk7TsBdbt8fKC6K2qQ9ExIMPyB DSC08771.ARW
Processing file 16JXludoMQAF21KdnrUigJTpzlim3tzka DSC08772.ARW
Processing file 1E4W0KhNFwxrBLvuCW8i06lzw2eyCRcve DSC08773.ARW
Processing file 1gDfTpKJdzvjoeTQyrmP0omkYmBceAggp DSC08774.ARW
Processing file 1uIGSgIEPS-Ug9B2UAVt5oq5Kafgr_CYx DSC08776.ARW
Processing file 1-pvS--NKGO2KYhgOZUCma2RNiDquWBdG DSC08777.ARW
Processing file 1kkpV4jQcTd48r2wXpNxBppPMsRVMSO9a DSC08778.ARW
Processing file 1BAoJWojNpwGJtgMsdPqJ8UK_cVoisfSS DSC08779.ARW
Processing file 1QvEESquEX3WZAgTrq76SRcMTOEZduGxa DSC08780.ARW
Processing file 10l2P9IkbBIAgIY-Uq_HWiPDtEW1WRsWa DSC08781.ARW
Processing file 1WqDPnFGChh8X1mLt0jVbFRgLLj1RvRkd DSC08782.ARW
Processing file 1yiTSf2U9NhyPtdiVtwXIicWapuTuA-Oo DSC08783.ARW
Processing file 1oVOabpl2hBu59KBJBJ-Nrs1io6sQAB4x DSC08784.ARW
Processing file 1x2uNmiOFGE5-u8edk28mLbWI9FxeYor9 DSC08785.ARW
Processing file 1tT7tKB3QggNMbqCydXoKakmNUTQ7AcI9 DSC08786.ARW
Processing file 1lfz5tHLKq73xYC7SGRfDLiSzXAn_MAvq DSC08787.ARW
Processing file 1TkodG3I4FVr3tA_-EwzNbj4UtrJz5tpn DSC08788.ARW
Processing file 1QZYig4eJY-M6Oy9n4Pj5aHjz2dGs15Jz DSC08789.ARW
Processing file 17JmKwjeEPwIXPVM-FdOn77a89KQcLfzL DSC08790.ARW
Processing file 1igk2PJq_jEimpURbLGPb1w-mWK3FqHIS DSC08791.ARW
Processing file 122hF1qOBxDosk2raWclhc26TXz2HmeoF DSC08792.ARW
Processing file 1KtK3_AHZHw85NU99YsD_G6MVkeIVVirr DSC08793.ARW
Processing file 1L-Pf1mbVZuyg-nI3_wHgBYrf8aAtb5Z2 DSC08794.ARW
Processing file 15B_vg6FYda4j9f17Mz9yW5giTQ_m3i8Y DSC08795.ARW
Processing file 1TsRgVf4L8GbU5IqrwzEdt6h0IFLFPimJ DSC08796.ARW
Processing file 1lir3RdEqTCu6mpNzEGCBMD33u-kd9lLf DSC08797.ARW
Processing file 1fqoxIw9d7_WsUmIcKD2-ZO4-0ZQEKdaJ DSC08798.ARW
Processing file 1k9QO5UkhoRUB3BZ6wEqK6Yt1-3ixxZc3 DSC08799.ARW
Processing file 1Fi0qAK5iKpI_HY_R9gG2LI5RzyqP9BkH DSC08800.ARW
Processing file 1qFjWwZjGkqLmSNfSqJultDFFUOw8xEfF DSC08801.ARW
Processing file 1nx-5PRXV8CTQnWEZpkw_2Lgw9er3IeYv DSC08802.ARW
Processing file 1pU66FSzI4Sj5ZaKag9SdFqHAp6POh8Ed DSC08803.ARW
Processing file 1Iv-ihEvrFmC6LhJaPoKJMCiG6AoBMnPG DSC08804.ARW
Processing file 15jBw_1dpwj67Ih1o0tcrrHfif6i70mU6 DSC08805.ARW

Retrieving folder contents completed
Building directory structure
Building directory structure completed
Downloading...
From: https://drive.google.com/uc?id=1rVbyYAovN0W8dsfIsV-6OUBisj4mbApm
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08770.ARW
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Downloading...
From: https://drive.google.com/uc?id=1eL1pyIDk7TsBdbt8fKC6K2qQ9ExIMPyB
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08771.ARW
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24.7M/24.7M [00:00<00:00, 46.7MB/s]
Downloading...
From: https://drive.google.com/uc?id=16JXludoMQAF21KdnrUigJTpzlim3tzka
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100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24.8M/24.8M [00:00<00:00, 69.0MB/s]
Downloading...
From: https://drive.google.com/uc?id=1E4W0KhNFwxrBLvuCW8i06lzw2eyCRcve
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08773.ARW
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24.8M/24.8M [00:00<00:00, 64.4MB/s]
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Downloading...
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100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24.8M/24.8M [00:00<00:00, 63.7MB/s]
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From: https://drive.google.com/uc?id=1-pvS--NKGO2KYhgOZUCma2RNiDquWBdG
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100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24.8M/24.8M [00:00<00:00, 58.8MB/s]
Downloading...
From: https://drive.google.com/uc?id=1BAoJWojNpwGJtgMsdPqJ8UK_cVoisfSS
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100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24.8M/24.8M [00:00<00:00, 67.2MB/s]
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From: https://drive.google.com/uc?id=1QvEESquEX3WZAgTrq76SRcMTOEZduGxa
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From: https://drive.google.com/uc?id=10l2P9IkbBIAgIY-Uq_HWiPDtEW1WRsWa
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08781.ARW
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From: https://drive.google.com/uc?id=1WqDPnFGChh8X1mLt0jVbFRgLLj1RvRkd
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08782.ARW
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From: https://drive.google.com/uc?id=1yiTSf2U9NhyPtdiVtwXIicWapuTuA-Oo
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08783.ARW
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From: https://drive.google.com/uc?id=1oVOabpl2hBu59KBJBJ-Nrs1io6sQAB4x
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08784.ARW
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From: https://drive.google.com/uc?id=1x2uNmiOFGE5-u8edk28mLbWI9FxeYor9
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08785.ARW
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From: https://drive.google.com/uc?id=1tT7tKB3QggNMbqCydXoKakmNUTQ7AcI9
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08786.ARW
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From: https://drive.google.com/uc?id=1lfz5tHLKq73xYC7SGRfDLiSzXAn_MAvq
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08787.ARW
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From: https://drive.google.com/uc?id=1TkodG3I4FVr3tA_-EwzNbj4UtrJz5tpn
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08788.ARW
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From: https://drive.google.com/uc?id=1QZYig4eJY-M6Oy9n4Pj5aHjz2dGs15Jz
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08789.ARW
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From: https://drive.google.com/uc?id=17JmKwjeEPwIXPVM-FdOn77a89KQcLfzL
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08790.ARW
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From: https://drive.google.com/uc?id=1igk2PJq_jEimpURbLGPb1w-mWK3FqHIS
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08791.ARW
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From: https://drive.google.com/uc?id=122hF1qOBxDosk2raWclhc26TXz2HmeoF
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08792.ARW
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From: https://drive.google.com/uc?id=1KtK3_AHZHw85NU99YsD_G6MVkeIVVirr
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08793.ARW
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From: https://drive.google.com/uc?id=1L-Pf1mbVZuyg-nI3_wHgBYrf8aAtb5Z2
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08794.ARW
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From: https://drive.google.com/uc?id=15B_vg6FYda4j9f17Mz9yW5giTQ_m3i8Y
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08795.ARW
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From: https://drive.google.com/uc?id=1TsRgVf4L8GbU5IqrwzEdt6h0IFLFPimJ
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08796.ARW
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From: https://drive.google.com/uc?id=1lir3RdEqTCu6mpNzEGCBMD33u-kd9lLf
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08797.ARW
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From: https://drive.google.com/uc?id=1fqoxIw9d7_WsUmIcKD2-ZO4-0ZQEKdaJ
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08798.ARW
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From: https://drive.google.com/uc?id=1k9QO5UkhoRUB3BZ6wEqK6Yt1-3ixxZc3
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08799.ARW
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From: https://drive.google.com/uc?id=1Fi0qAK5iKpI_HY_R9gG2LI5RzyqP9BkH
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08800.ARW
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From: https://drive.google.com/uc?id=1qFjWwZjGkqLmSNfSqJultDFFUOw8xEfF
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08801.ARW
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To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08802.ARW
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From: https://drive.google.com/uc?id=1pU66FSzI4Sj5ZaKag9SdFqHAp6POh8Ed
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08803.ARW
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From: https://drive.google.com/uc?id=1Iv-ihEvrFmC6LhJaPoKJMCiG6AoBMnPG
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08804.ARW
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From: https://drive.google.com/uc?id=15jBw_1dpwj67Ih1o0tcrrHfif6i70mU6
To: C:\Users\Timothy Do\Documents\GitHub\astronomy\data\custom\mtPinos\DSC08805.ARW
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Download completed

testImgPath = f'{mtPinosDir}/DSC08770.ARW'
testImg = cr2_to_numpy(testImgPath,use_camera_wb=True,no_auto_bright=False)
plt.figure(figsize=(15,10))
plt.imshow(testImg)
plt.axis('off')
plt.title('Mt. Pinos Test Scene')
plt.show()

# Stacking Custom Images
stackedFiles = sorted([f'{mtPinosDir}/{file}' for file in os.listdir(mtPinosDir) if file.endswith('.ARW')])
stackedImg = stack_images_opt(stackedFiles,use_camera_wb=True,no_auto_bright=False)

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# Plotting the Stacked Image
plt.figure(figsize=(15,10))
plt.title(f'Mt. Pinos Stacked Image')
plt.imshow(contrast_stretch(stackedImg))
plt.axis('off')
plt.show()

# Writing the Final Result
cv.imwrite(f'{mtPinosDir}/stackedImg.tiff',float_to_int16(stackedImg)[...,::-1])
print('Saving the Final Result')

Saving the Final Result